AN UNSUPERVISED NEURAL NETWORK TO PREDICT THE LEVEL OF HEAT STRESS

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Abstract

Heat stress is known to induce high mortality rate due to multi-system illness, which demands urgent attention to reduce the fatality rate in such patients. Further, for the diagnosis and supportive therapy, one needs to define the severity of heat stress that can be distinguished as mild, intermediate and severe. The objective of this work is to develop an automated unsupervised artificial system to analyze the clinical outcomes of different levels of heat related illnesses. The Kohonen neural network program written in C++, which has seven normalized values of different clinical symptoms between 0-1 fed to the input layer of the network with 50 Kohonen output neurons, has been presented. The optimized initializing parameters such as neighborhood size and learning rate was set to 50 and 0.7, respectively, to simulate the network for 10 million iterations. The network was found smartly distinguishing all 51 patterns to three different states of heat illnesses. With the advent of these findings, it can be concluded that the Kohonen neural network can be used for automated classification of the severity of heat stress and other related psycho-patho-physiological disorders. However, to replace the expert clinicians with such type of smart diagnostic tool, extensive work is required to optimize the system with variety of known and hidden clinical and pathological parameters.

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